{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2db6c902-9d8f-4ed3-8f42-7ff59612141e",
   "metadata": {},
   "source": [
    "## PixelCNN\n",
    "\n",
    "PixelCNN is an autoregressive likelihood model for the task of image modeling.\n",
    "\n",
    "### Reference\n",
    "- https://keras.io/examples/generative/pixelcnn/\n",
    "- Pixel Recurrent Neural Networks: https://arxiv.org/abs/1601.06759"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff8aa709-5726-4581-9d2a-f0503a591008",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, transforms\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bb28827-5f13-4a50-91bf-146b18ce9a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MaskedConv2d(nn.Conv2d):\n",
    "    \"\"\"\n",
    "    Implements a conv2d with mask applied on its weights.\n",
    "    \n",
    "    Args:\n",
    "        mask_type (str): the mask type, 'A' or 'B'.\n",
    "        in_channels (int) – Number of channels in the input image.\n",
    "        out_channels (int) – Number of channels produced by the convolution.\n",
    "        kernel_size (int or tuple) – Size of the convolving kernel\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, mask_type, in_channels, out_channels, kernel_size, **kwargs):\n",
    "        super().__init__(in_channels, out_channels, kernel_size, **kwargs)\n",
    "        self.mask_type = mask_type\n",
    "        \n",
    "        if isinstance(kernel_size, int):\n",
    "            kernel_size = (kernel_size, kernel_size)\n",
    "        mask = torch.zeros(kernel_size)\n",
    "        mask[:kernel_size[0]//2, :] = 1.0\n",
    "        mask[kernel_size[0]//2, :kernel_size[1]//2] = 1.0\n",
    "        if self.mask_type == \"B\":\n",
    "            mask[kernel_size[0]//2, kernel_size[1]//2] = 1.0\n",
    "        self.register_buffer('mask', mask[None, None])\n",
    "        \n",
    "    def forward(self, x):\n",
    "        self.weight.data *= self.mask # mask weights\n",
    "        return super().forward(x)\n",
    "    \n",
    "\n",
    "class ResidualBlock(nn.Module):\n",
    "    \"\"\"\n",
    "    Residual Block: conv1x1 -> conv3x3 -> conv1x1\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, in_channels):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels, in_channels // 2, 1),\n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "        # masked conv2d\n",
    "        self.conv2 = nn.Sequential(\n",
    "            MaskedConv2d(\"B\", in_channels // 2, in_channels // 2, 3, padding=1),\n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels // 2, in_channels, 1),\n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        inputs = x\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        return inputs + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56c8d21a-232c-4558-9ac9-da658f576572",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PixelCNN(nn.Module):\n",
    "    \"\"\"\n",
    "    PixelCNN model\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, in_channels=1, channels=128, out_channels=1, n_residual_blocks=5):\n",
    "        super().__init__()\n",
    "        \n",
    "        # we use maskedconv \"A\" for the first layer\n",
    "        self.stem = nn.Sequential(\n",
    "            MaskedConv2d(\"A\", in_channels, channels, 7, padding=3),\n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "        self.res_blocks = nn.Sequential(\n",
    "            *[ResidualBlock(channels) for _ in range(n_residual_blocks)]\n",
    "        )\n",
    "        self.head = nn.Sequential(\n",
    "            MaskedConv2d(\"B\", channels, channels, 3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            MaskedConv2d(\"B\", channels, channels, 3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(channels, out_channels, 1)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.stem(x)\n",
    "        x = self.res_blocks(x)\n",
    "        x = self.head(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f3d8d7da-625d-4506-8db7-5f582bf42985",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_size = 28\n",
    "in_channels = 1\n",
    "out_channels = 1\n",
    "channels = 128 # hidden channels\n",
    "n_residual_blocks = 5\n",
    "\n",
    "batch_size = 128\n",
    "epochs = 50\n",
    "\n",
    "transform=transforms.Compose([\n",
    "    transforms.ToTensor()\n",
    "])\n",
    "\n",
    "dataset1 = datasets.MNIST('/data', train=True, download=True,\n",
    "                       transform=transform)\n",
    "dataset2 = datasets.MNIST('/data', train=False,\n",
    "                       transform=transform)\n",
    "train_loader = torch.utils.data.DataLoader(dataset1, batch_size=batch_size, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(dataset2, batch_size=batch_size)\n",
    "\n",
    "model = PixelCNN(in_channels, channels, out_channels, n_residual_blocks).cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7158b9d0-9dd1-4060-8d7e-dd6f0f185d4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start training epoch 0\n",
      "\t [468/469]: loss 0.09943267703056335\n",
      "Start training epoch 1\n",
      "\t [468/469]: loss 0.08993204683065414\n",
      "Start training epoch 2\n",
      "\t [468/469]: loss 0.08632215857505798\n",
      "Start training epoch 3\n",
      "\t [468/469]: loss 0.0857618898153305\n",
      "Start training epoch 4\n",
      "\t [468/469]: loss 0.08323406428098679\n",
      "Start training epoch 5\n",
      "\t [468/469]: loss 0.08515626192092896\n",
      "Start training epoch 6\n",
      "\t [468/469]: loss 0.09080298990011215\n",
      "Start training epoch 7\n",
      "\t [468/469]: loss 0.0851127877831459\n",
      "Start training epoch 8\n",
      "\t [468/469]: loss 0.08575288951396942\n",
      "Start training epoch 9\n",
      "\t [468/469]: loss 0.08795469254255295\n",
      "Start training epoch 10\n",
      "\t [468/469]: loss 0.08480170369148254\n",
      "Start training epoch 11\n",
      "\t [468/469]: loss 0.08301049470901489\n",
      "Start training epoch 12\n",
      "\t [468/469]: loss 0.08615297824144363\n",
      "Start training epoch 13\n",
      "\t [468/469]: loss 0.08480261266231537\n",
      "Start training epoch 14\n",
      "\t [468/469]: loss 0.08743501454591751\n",
      "Start training epoch 15\n",
      "\t [468/469]: loss 0.08629097044467926\n",
      "Start training epoch 16\n",
      "\t [468/469]: loss 0.08073026686906815\n",
      "Start training epoch 17\n",
      "\t [468/469]: loss 0.08512020856142044\n",
      "Start training epoch 18\n",
      "\t [468/469]: loss 0.08142197132110596\n",
      "Start training epoch 19\n",
      "\t [468/469]: loss 0.0807342529296875\n",
      "Start training epoch 20\n",
      "\t [468/469]: loss 0.08460844308137894\n",
      "Start training epoch 21\n",
      "\t [468/469]: loss 0.08269714564085007\n",
      "Start training epoch 22\n",
      "\t [468/469]: loss 0.08292073011398315\n",
      "Start training epoch 23\n",
      "\t [468/469]: loss 0.08164182305335999\n",
      "Start training epoch 24\n",
      "\t [468/469]: loss 0.08252546936273575\n",
      "Start training epoch 25\n",
      "\t [468/469]: loss 0.08031551539897919\n",
      "Start training epoch 26\n",
      "\t [468/469]: loss 0.07918865978717804\n",
      "Start training epoch 27\n",
      "\t [468/469]: loss 0.08197366446256638\n",
      "Start training epoch 28\n",
      "\t [468/469]: loss 0.08056250959634781\n",
      "Start training epoch 29\n",
      "\t [468/469]: loss 0.0826050266623497\n",
      "Start training epoch 30\n",
      "\t [468/469]: loss 0.08208416402339935\n",
      "Start training epoch 31\n",
      "\t [468/469]: loss 0.08351639658212662\n",
      "Start training epoch 32\n",
      "\t [468/469]: loss 0.07901784777641296\n",
      "Start training epoch 33\n",
      "\t [468/469]: loss 0.08264601230621338\n",
      "Start training epoch 34\n",
      "\t [468/469]: loss 0.0835525318980217\n",
      "Start training epoch 35\n",
      "\t [468/469]: loss 0.08255098015069962\n",
      "Start training epoch 36\n",
      "\t [468/469]: loss 0.0846540629863739\n",
      "Start training epoch 37\n",
      "\t [468/469]: loss 0.08017908781766891\n",
      "Start training epoch 38\n",
      "\t [468/469]: loss 0.0825449600815773\n",
      "Start training epoch 39\n",
      "\t [468/469]: loss 0.08127618581056595\n",
      "Start training epoch 40\n",
      "\t [468/469]: loss 0.07939404249191284\n",
      "Start training epoch 41\n",
      "\t [468/469]: loss 0.08136747777462006\n",
      "Start training epoch 42\n",
      "\t [468/469]: loss 0.08235418796539307\n",
      "Start training epoch 43\n",
      "\t [468/469]: loss 0.0813743844628334\n",
      "Start training epoch 44\n",
      "\t [468/469]: loss 0.08264637738466263\n",
      "Start training epoch 45\n",
      "\t [468/469]: loss 0.07891112565994263\n",
      "Start training epoch 46\n",
      "\t [468/469]: loss 0.08211299031972885\n",
      "Start training epoch 47\n",
      "\t [468/469]: loss 0.08249464631080627\n",
      "Start training epoch 48\n",
      "\t [468/469]: loss 0.07970467209815979\n",
      "Start training epoch 49\n",
      "\t [468/469]: loss 0.08078432828187943\n"
     ]
    }
   ],
   "source": [
    "print_freq = 500\n",
    "for epoch in range(epochs):\n",
    "    print(\"Start training epoch {}\".format(epoch,))\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        images = (images > 0.33).float() # convert to 0, 1\n",
    "        images = images.cuda()\n",
    "        logits = model(images)\n",
    "        loss = F.binary_cross_entropy_with_logits(logits, images)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if (i + 1) % print_freq == 0 or (i + 1) == len(train_loader):\n",
    "            print(\"\\t [{}/{}]: loss {}\".format(i, len(train_loader), loss.item()))       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d148f2e-25b4-461e-bc9c-79769f2e57b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## generate new images by PixelCNN\n",
    "n_cols, n_rows = 8, 8\n",
    "C = 1\n",
    "H = 28\n",
    "W = 28\n",
    "\n",
    "# Create an empty array of pixels.\n",
    "pixels = torch.zeros(n_cols * n_rows, C, H, W).cuda()\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    # Iterate over the pixels because generation has to be done sequentially pixel by pixel.\n",
    "    for h in range(H):\n",
    "        for w in range(W):\n",
    "            for c in range(C):\n",
    "                # Feed the whole array and retrieving the pixel value probabilities for the next pixel.\n",
    "                logits = model(pixels)[:, c, h, w]\n",
    "                probs = logits.sigmoid()\n",
    "                # Use the probabilities to pick pixel values and append the values to the image frame.\n",
    "                pixels[:, c, h, w] = torch.bernoulli(probs)\n",
    "                \n",
    "generated_imgs = pixels.cpu().numpy()\n",
    "generated_imgs = np.array(generated_imgs * 255, dtype=np.uint8).reshape(n_rows, n_cols, H, W)\n",
    "    \n",
    "fig = plt.figure(figsize=(8, 8), constrained_layout=True)\n",
    "gs = fig.add_gridspec(n_rows, n_cols)\n",
    "for n_col in range(n_cols):\n",
    "    for n_row in range(n_rows):\n",
    "        f_ax = fig.add_subplot(gs[n_row, n_col])\n",
    "        f_ax.imshow(generated_imgs[n_row, n_col], cmap=\"gray\")\n",
    "        f_ax.axis(\"off\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
